Mercor is seeking experimental scientists and engineers specialising in superconductors to support a frontier AI research lab building advanced models for materials science and the physical sciences. This is hands-on, expert-level work: you’ll apply deep, specialised knowledge of superconducting materials, synthesis, characterisation, transport measurements, thin films, and superconducting devices to generate, structure, and evaluate the scientific data these models learn from — and your input will directly shape how advanced models reason about superconducting materials, properties, experiments, and devices.
Key Responsibilities:
Contribute deep domain expertise across superconducting material synthesis, characterisation, experimental superconductivity, fabrication, and device physics to build high-quality training and evaluation data.
Review and evaluate AI-generated scientific reasoning related to superconducting materials and phenomena , identifying experimental or theoretical errors and improving technical accuracy.
Design and solve challenging, expert-level problems involving critical temperature (Tc), critical current density (Jc), critical magnetic fields, magnetic susceptibility, electrical transport, flux pinning, vortex behaviour, and structure-property relationships .
Analyse and reason about experimental results from techniques such as XRD, SEM/TEM, SQUID/VSM magnetometry, resistivity and magnetotransport measurements, spectroscopy, and cryogenic measurements .
Rate and rank model outputs against defined scientific criteria, providing clear written reasoning grounded in experimental superconductivity.
Structure technical knowledge — including synthesis procedures, processing parameters, phase and structural data, transport measurements, magnetic measurements, and device data — into well-organised, model-ready data.
Deliver reliable, technically rigorous, high-quality work within defined timelines.
You’re a strong fit if you have:
Hands-on experimental experience with one or more classes of superconducting materials , including cuprate/high-temperature superconductors, iron-based superconductors, conventional superconductors, MgB2, REBCO/YBCO systems, or emerging superconducting materials .
Experience with superconducting material synthesis or processing , such as solid-state synthesis, single-crystal growth, powder processing, thin-film deposition, or related techniques.
Strong experience characterising superconducting properties through electrical resistivity/transport, magnetotransport, magnetic susceptibility, SQUID/VSM magnetometry, critical current measurements, critical field measurements, or cryogenic experiments .
Strong understanding of superconducting phenomena, including Tc, Jc, critical magnetic fields, Meissner effect, flux pinning, vortex dynamics, and the relationship between material structure and superconducting performance .
Experience with superconducting thin films, heterostructures, or device fabrication using techniques such as PLD, MBE/epitaxy, sputtering, lithography, etching, or clean-room micro/nanofabrication — a plus.
Experience with applied superconductors or superconducting devices , such as superconducting wires and tapes, coated conductors, Josephson junctions, SQUIDs, superconducting circuits, resonators, detectors, or superconducting quantum devices — a plus.
An advanced degree (PhD/MS ) or equivalent hands-on research experience in materials science, condensed matter physics, applied physics, chemistry, electrical engineering, or a closely related field.
Clear written English and the ability to explain complex experimental results and technical reasoning concisely.
Why Join:
Shape frontier AI for superconductivity: Apply your expertise to improve how advanced AI models reason about superconducting materials, experiments, properties, and devices.
Work on technically challenging problems: Engage with expert-level questions spanning superconducting synthesis, characterisation, transport, cryogenic measurements, thin films, and device physics.
Translate experimental expertise into AI capability: Your hands-on research experience will directly contribute to creating scientifically rigorous training and evaluation data for next-generation models.
Long-term research opportunity: Contribute to an ongoing collaboration at the intersection of superconductivity, materials science, and frontier AI research .